A Probabilistic Graph Model for Trust Opinion Estimation in Online Social Networks

TL;DR

This paper introduces a probabilistic graph model to improve trust opinion estimation in online social networks, enabling more accurate trust assessments for applications like lending and product reviews.

Contribution

It proposes a novel framework that converts trust parameters into a probabilistic graph model to better capture trustor biases and opinions.

Findings

Enhanced trust estimation accuracy demonstrated

Framework effectively models trustor biases

Applicable to various online trust-based applications

Abstract

Trust assessment plays a key role in many online applications, such as online money lending, product reviewing and active friending. Trust models usually employ a group of parameters to represent the trust relation between a trustor-trustee pair. These parameters are originated from the trustor's bias and opinion on the trustee. Naturally, these parameters can be regarded as a vector. To address this problem, we propose a framework to accurately convert the single values to the parameters needed by 3VSL. The framework firstly employs a probabilistic graph model (PGM) to derive the trustor's opinion and bias to his rating on the trustee.